Modern businesses use contact centers as a communication channel with users of their products and services. In recent years, contact centers have grown in importance as an interface of an organization to its customers, both existing and new. A customer's experience with the contact center is seen as a factor in customer retention, as well as an opportunity to sell more to existing customers (cross-sell, up-sell) and to sell to new customers. The largest factor in the expense of running a contact center is the labor cost of its customer service agents.
In most contact centers, every interaction (e.g., call, chat session, e-mail or written letter) with a customer service agent has to be documented in an interaction log. Such a log can be for one or more purposes, such as to support later continuation of the same customer interaction by different agents as well as the same agent, to support transfer of the case to another agent, or to provide legally-mandated documentation of the interaction. Agents may variously create logs during the interaction, while the customer is on hold (if a phone interaction), or immediately after the interaction is completed. Time spent for logging is typically tracked by contact center management in order to identify and improve upon inefficiencies.
Many contact centers use a customer relationship management (CRM) system to maintain customer interaction information, including the interaction logs. An agent may create the log directly, or make notes in a text editor on an agent computer during the interaction and then transfer the information into a CRM record with cut-and-paste operations. A typical CRM record consists of a collection of fields, called a “template,” containing data to identify the customer and the customer's purchased item. In an example automobile provider/service environment, the CRM record might contain template fields for the make, model and VIN (vehicle identification number) of the customer's vehicle, owner information such as name, telephone number, and warranty status, and at least one open-input field for free-form documentation including such information as the customer's statement of the problem, relevant facts gathered from the customer (e.g. dealer, repair shop), and the agent's resolution of the customer's problem (e.g., gave rebate on repair cost). The free-form documentation together with the template fields is referred to as a log (or call log if the interaction is via a telephone).
While the free-form documentation is text, it is often quite irregular, characterized by idiosyncratic abbreviations, misspellings, missing words, grammatical errors, and incorrect case. The agent's goal is speed and completeness of documentation, not readability. Due to agent haste and errors, interaction logs often do not consistently capture appropriate and accurate information. Hence, automated or semi-automated creation of contact center interaction logs is desirable for at least two reasons: (1) it can potentially save contact centers money (by reducing the time spent creating the logs) and (2) it can improve the quality of the logs by creating less error-prone and more consistent documentation.
Related systems for summarizing speeches or dialogues in other environments are well known. For example, summarization of speech has been employed for broadcast news and voice mail. For broadcast news, approaches range from ones derived largely from text summarization to strategies combining prosodic, lexical, and structural features to other approaches having exclusive reliance on acoustic/prosodic features. In dialog summarization, a related application of summarization, prior art approaches rely on tf/idf (term frequency/inverse document frequency) scores, tempered with cross-speaker information linking and question/answer detection. While contact center interaction logging is similar to dialogue summarization, there are notable differences. First, the summarization of contact center interactions is highly dependent on the particular terms of interest in the industry or even the company represented by the contact center; for example, a VIN is a critically important item in a car manufacturer's interaction log. Hence, automated production of contact center logs requires the use of industry- or company-specific terminology. Second, contact center interactions are often highly scripted (i.e., follow prescribed patterns or business processes for dealing with the customers) and particular details of the scripted process need to be reflected in the log (e.g. agent asking for phone number or credit card number). Hence, unlike general open-domain dialogue summarization, which needs only to determine domain salience statistically, contact center log generation needs to identify important items from the scripted process in the interaction and attach semantic labels to information required by the script to route identified items into the correct fields of the CRM record. Third, because contact center managements often develop best practices for creating logs, and these best practices may again be industry- or company-specific, automated log creation must reflect those best practices. And fourth, because the environment in contact centers can change rapidly to reflect changing products, customer issues, customer buying patterns, advertising campaigns and even world events, the system for creating the contact center logs must adapt over time; hence, feedback systems are desired to ensure continued adaptation of the system.
A common method for creating adaptive systems is to create models, which may be thought of as sets of rules or patterns for creating the desired type of output (log in this case) from a given input (here, the text of the full interaction). In the present invention, two different types of models are described: a global model which contains the sets of rules and patterns to be applied across all logs created by the system, and a real-time model, which affects the creation of only the log for the current interaction. Some elements of the global model may, if desired, be considered fixed; such that they can not be overridden by user feedback. Non-fixed elements for both types of models can preferably be updated based on feedback from users of the system; hence the models “learn” or adapt as the system is used. The real-time model is updated during the analysis of an individual call, whereas the global model is updated after one or more calls have been completed.
It is an object therefore of the present invention to provide a system and method for automatically and adaptively (using feedback) generating a log from the interaction text of an interaction between a customer and the contact center agent that meets changing requirements for contact center logs.